This model was obtained by quantizing the weights and activations of Bielik-11B-v.2.2-Instruct to FP8 data type, ready for inference with vLLM >= 0.5.0 or SGLang. AutoFP8 is used for quantization. This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%. Only the weights and activations of the linear operators within transformers blocks are quantized. Symmetric per-tensor quantization is applied, in which a single linear scaling maps the FP8 representations of the quantized weights and activations. FP8 compuation is supported on Nvidia GPUs with compute capability > 8.9 (Ada Lovelace, Hopper). DISCLAIMER: Be aware that quantised models show reduced response quality and possible hallucinations! This model can be deployed efficiently using the vLLM backend, as shown in the example below. vLLM aslo supports OpenAI-compatible serving. See the documentation for more details. Then you can send http request or use OpenAI Compatible API. Developed by: SpeakLeash & ACK Cyfronet AGH Language: Polish Model type: causal decoder-only Quant from: Bielik-11B-v2.2-Instruct Finetuned from: Bielik-11B-v2 …
Модальности:
Генерация текста
Области применения:
Диалог / чат Следование инструкциям
Задача: Генерация текста
Автор: speakleash
Теги: mistral, finetuned, gguf, 8bit, conversational, pl, text-generation-inference, fp8
Лайков: 3 | Загрузок: 74
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
